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How information influences the cost of transport in a supply chain, a monte carlo simulation

Author

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  • Xavier Brusset

    (IAG, Université Catholique de Louvain, Louvain la Neuve, Belgium)

Abstract

The present paper studies the impact of information sharing and contractual instruments on a shipper and her transport suppliers through a monte carlo simulation. After reviewing the literature, we propose a model to measure the benefits in terms of expected transport cost and variance of this cost. We evaluate three scenarios over a reiterated- single period setting in a shipper carrier single-echelon model with a mix of long-term and short-term procurement strategies: perfect information, asymmetric information and private information at one level of the supply chain. After spelling out the optimal parameters for the procurement policy, we evaluate the rent transfer between carrier and shipper in a numeric example using the monte-carlo method.

Suggested Citation

  • Xavier Brusset, 2005. "How information influences the cost of transport in a supply chain, a monte carlo simulation," Econometrics 0512008, EconWPA.
  • Handle: RePEc:wpa:wuwpem:0512008
    Note: Type of Document - pdf; pages: 49. A monte carlo simulation to show the importance of asymetrical information in transport cost and distribution of this cost between a shipper and a carrier.
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    File URL: http://econwpa.repec.org/eps/em/papers/0512/0512008.pdf
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    References listed on IDEAS

    as
    1. Gérard P. Cachon & Martin A. Lariviere, 2001. "Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain," Management Science, INFORMS, vol. 47(5), pages 629-646, May.
    2. Wu, D. J. & Kleindorfer, P. R. & Zhang, Jin E., 2002. "Optimal bidding and contracting strategies for capital-intensive goods," European Journal of Operational Research, Elsevier, vol. 137(3), pages 657-676, March.
    3. A. A. Tsay & W. S. Lovejoy, 1999. "Quantity Flexibility Contracts and Supply Chain Performance," Manufacturing & Service Operations Management, INFORMS, vol. 1(2), pages 89-111.
    4. Bryan R. Routledge & Duane J. Seppi & Chester S. Spatt, 2000. "Equilibrium Forward Curves for Commodities," Journal of Finance, American Finance Association, vol. 55(3), pages 1297-1338, June.
    5. Grieger, Martin, 2003. "Electronic marketplaces: A literature review and a call for supply chain management research," European Journal of Operational Research, Elsevier, vol. 144(2), pages 280-294, January.
    6. Seifert, Ralf W. & Thonemann, Ulrich W. & Hausman, Warren H., 2004. "Optimal procurement strategies for online spot markets," European Journal of Operational Research, Elsevier, vol. 152(3), pages 781-799, February.
    7. Kamran Moinzadeh & Steven Nahmias, 2000. "Adjustment Strategies for a Fixed Delivery Contract," Operations Research, INFORMS, vol. 48(3), pages 408-423, June.
    8. Gérard P. Cachon & Paul H. Zipkin, 1999. "Competitive and Cooperative Inventory Policies in a Two-Stage Supply Chain," Management Science, INFORMS, vol. 45(7), pages 936-953, July.
    9. repec:mes:jeciss:v:30:y:1996:i:4:p:1212-1216 is not listed on IDEAS
    10. Andy A. Tsay, 1999. "The Quantity Flexibility Contract and Supplier-Customer Incentives," Management Science, INFORMS, vol. 45(10), pages 1339-1358, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    supply chain management; transport; contract; monte carlo; bivariate normal distribution; information;

    JEL classification:

    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium

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